Community

Data Analysis Tools

CDAT is a powerful and complete front-end to a rich set of visual-data exploration and analysis capabilities well suited for data analysis problems.

Welcome to CDAT!

New here? Don’t worry! We’ll help you get started. If you’re interested in what you can do with CDAT, you can take a look at our gallery. If you’re interested in anything you see there, you can look into getting our application installed. Once CDAT has been installed, check out our Getting Started guide.

We'll give you a hand.

Having trouble with something? We’ve got a great community of people who can give you a hand on our support email list.

We're on GitHub!

If you want to get to know us, you can come chat with us on our developer mail list. If you want to get up to speed with the project, our wiki is kept up-to-date with what you need to get going.

The CDAT Project

CDAT builds on the following key technologies:

Python and its ecosystem (e.g. NumPy, Matplotlib);

Jupyter Notebooks and iPython;

A toolset developed at LLNL for the analysis, visualization, and management of large-scale distributed climate data;

VTK, the Visualization Toolkit, which is open source software for manipulating and displaying scientific data.

These combined tools, along with others such as the R open-source statistical
analysis and plotting software and custom packages (e.g. DV3D), form CDAT
and provide a synergistic approach to climate modeling, allowing researchers to
advance scientific visualization of large-scale climate data sets. The CDAT
framework couples powerful software infrastructures through two primary means:

Loosely coupled integration to provide the flexibility of using tools quickly
in the infrastructure such as ViSUS or R for data analysis and
visualization as well as to apply customized data analysis applications within
an integrated environment.

Within both paradigms, CDAT will provide data-provenance capture and
mechanisms to support data analysis.